Monitoring system

ABSTRACT

A monitoring system is provided, which may include a structural component configured to undergo mechanical loading and a wireless node attached to the structural component. The node may include a strain sensing device configured to measure strain experienced by the structural component at the location of the node. The node may also include a processor configured to predict, based on the strain measurements, fatigue life of the structural component.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is related to U.S. patent application Ser. No.11/227,157, filed Sep. 16, 2005 entitled SYSTEMS AND METHODS FORDETERMINING FATIGUE LIFE, Attorney Docket No. 05-176; U.S. patentapplication Ser. No. 11/227,155, filed Sep. 16, 2005 entitledCLASSIFYING A WORK MACHINE OPERATION, Attorney Docket No. 05-177; andU.S. patent application Ser. No. 11/227,269, filed, Sep. 16, 2005entitled SYSTEMS AND METHODS FOR MAINTAINING LOAD HISTORIES, AttorneyDocket No. 05-178, all of which claim priority to U.S. ProvisionalPatent Application No. 60/675,493, filed Apr. 28, 2005. Each of theabove-listed applications are herein incorporated by reference in theirentirety.

GOVERNMENT RIGHTS

This invention was made with U.S. Government support under cooperativeagreement no. 70NANB2H3064 awarded by the National Institute ofStandards and Technology (NIST).

The U.S. Government has certain rights in the invention.

TECHNICAL FIELD

This disclosure relates generally to a monitoring system and, moreparticularly, to a monitoring system including wireless nodes formonitoring loads experienced by a structural component.

BACKGROUND

Machines, such as construction machines (e.g., tractors, dozers,loaders, earth movers, or other such pieces of equipment), may have anynumber of structural components that are subject to fatigue damage whichcould lead to structural failures. One method for monitoring fatiguedamage on a machine structure is to perform a manual, visual inspection.However, such a method may be impractical for several reasons. First,such an inspection may not be as comprehensive as desired. This may bedue, in part, to the difficulty of accessing some components of themachine, such as when the structure in question is concealed and cannotbe viewed without dismantling a portion of the machine. Second, a manualinspection of structural components can only be performed on a periodicbasis, yet damage and resulting catastrophic failure still can occurbetween inspections. Third, a manual inspection may not be able todetect how much fatigue damage may have already occurred in the machine,or predict the fatigue life of one or more machine components based onthe fatigue damage. While manual inspection may provide some insightinto damage that is visible to an inspector, (e.g., large visible cracksin a machine component), internal damage may not be readily apparentthrough manual inspection (e.g., small internal cracks in a component).

Some systems have been proposed utilizing various ways of monitoringstructures electronically to detect fatigue damage. However, theseproposed systems have not adequately addressed the monitoring ofstructures with rapidly changing load pictures, such as movablemachines. This is due in part to the way these proposed systems collectdata about the structure. These proposed systems may collect data aboutthe structure at a relatively low sampling rate to ease the computingburden of performing analysis on the data and storing the analysisresults. However, a low sampling rate may entirely miss some load stateswhich endure very briefly.

Many critical load states experienced by a machine may only endure verybriefly. For example, when a wheel loader is digging and the bucket hitsa rock, the load state may peak for a few brief moments before the rockis broken or dug out. In structures with rapidly changing load states,the sampling rate must be high in order to capture these peak loadstates which may endure only very briefly. If the sampling rate is tooslow to capture all or most of these critical load states, the analysisresults will not accurately reflect the true condition of the structure.Therefore, high sampling rates may be more appropriate for detecting thetrue condition of the structure, which may facilitate an accuratefatigue evaluation.

In addition, some machines have structural components that are used inharsh environments. For example, a forestry machine may be operatedamongst trees and bushes with branches that can damage wires that supplypower to and carry data from strain sensing devices. Therefore systemshave been developed that utilize wireless strain sensing devices.However, as mentioned, high sampling rates can collect large volumes ofdata that can be difficult to process, and/or transmit, especiallywirelessly. Wirelessly transmitting high volumes of data that resultfrom rapid sampling may not always be practical or possible. Inaddition, supplying power to such wireless strain sensing devices mayalso present a challenge.

Systems have been developed including wireless nodes having strainsensing devices that are configured to monitor strain and process straindata at the site of measurement to reduce the volume of data to betransmitted. For example, U.S. Patent Application Publication No.2005/0017602 to Arms et al. (“the '062 document”) discloses a systemconfigured to monitor peak strain or strain accumulation. The system ofthe '062 document may include a processor at the nodes for processingdata acquired by the strain sensing devices.

Although the '062 document discloses a system that may be configured tomonitor various aspects of fatigue, the '062 document is not configuredto predict fatigue life of a structural component using the processorsat the nodes. In fact, the '062 document does not discuss predictingfatigue life at all. Predicted fatigue life can be utilized to planmaintenance schedules and preventative maintenance, such as replacementof parts. However, the system of the '062 document does not disclosemaking such a prediction.

The present disclosure is directed to solving one or more of theproblems described above.

SUMMARY OF THE INVENTION

In one aspect, the present disclosure is directed to a monitoring systemincluding a structural component configured to undergo mechanicalloading and a wireless node attached to the structural component. Thenode may include a strain sensing device configured to measure strainexperienced by the structural component at the location of the node. Thenode may also include a processor configured to predict, based on thestrain measurements, fatigue life of the structural component.

In another aspect, the present disclosure is directed to a method ofevaluating fatigue in a structural component configured to undergomechanical loading. The method may include taking measurements of strainexperienced by the structural component at a node attached to thestructural component. The method may also include processing datacollected regarding the strain measurements with a processor located atthe node. The method may further include predicting, with the processor,fatigue life of the structural component based on the strainmeasurements.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagrammatic illustration of a machine according toconsistent with certain disclosed embodiments.

FIG. 2 is a block diagram illustration of a monitoring system consistentwith certain disclosed embodiments.

FIG. 3 is a diagrammatic illustration of an exemplary structuralcomponent consistent with certain disclosed embodiments.

FIG. 4 is a block diagram of an exemplary monitoring system consistentwith certain disclosed embodiments.

FIG. 5 is a summary flow chart of exemplary processes performed bymethods and systems consistent with certain disclosed embodiments.

FIG. 6 is a flow chart of an exemplary process for determining unknownloads, consistent with certain disclosed embodiments.

FIG. 7 is a flowchart of an exemplary strain calculation processconsistent with certain disclosed embodiments.

FIG. 8 is a flowchart of an exemplary neural network configurationprocess consistent with certain disclosed embodiments.

FIG. 9 is a flow chart of an exemplary operation classification processconsistent with certain disclosed embodiments.

FIG. 10 is a flow chart of an exemplary payload determination processconsistent with certain disclosed embodiments.

FIG. 11 is a diagrammatic illustration of an exemplary body under loadconsistent with certain disclosed embodiments.

FIG. 12 is a flow chart of an exemplary load history building processconsistent with certain disclosed embodiments.

FIG. 13 is a block diagram of an exemplary damage rate histogramconsistent with certain disclosed embodiments.

FIG. 14 is a flow chart of an exemplary data analysis process consistentwith certain disclosed embodiments.

DETAILED DESCRIPTION

Reference will now be made in detail to exemplary embodiments andillustrations. Wherever possible, the same reference numbers will beused throughout the drawings to refer to the same or like parts. Whilespecific configurations and arrangements are discussed, it should beunderstood that this is done for illustrative purposes only.

Methods and systems consistent with the disclosed embodiments performprocesses that determine, among other things, loads, health, and use ofa machine or components of a machine. In one embodiment, a machine maybe outfitted with a number of sensors. Some of the sensors may measureinformation reflecting the orientation and movement of the machine, suchas inclination relative to the ground, and the positions of the movableparts of the machine. Other sensors may measure information about forcesacting on the machine. Additional sensors may also measure the strainexperienced by certain components of the machine.

Certain forces acting on the machine, however, may be difficult todirectly measure using these sensors, such as ground engaging forcesacting on the machine's wheels. The disclosed embodiments overcome thisproblem by using the measured forces and strains, along with otherinformation, such as orientation information, to determine unmeasuredforces. The unmeasured forces may be calculated using, for example,traditional Newtonian force balance and stress-strain calculations.Moreover, in certain embodiments, a neural network may be used to morequickly solve for these unknown forces.

Accordingly, the type of data used and determined in certain disclosedembodiments may include different types of data. One type may bemeasured data associated with raw measured information reflecting forcesexperienced by a component or structure (e.g., pressure in a cylinder,etc.). A second type of data may be measured strain data associated withactual strains experienced by a component or structure. For example,sensors on opposite ends of a component may provide measured informationthat reflect the strain experienced by that component. Collectively, thefirst and second types of data may define the constraints of the stateof a given body (e.g., a component, set of components, the entiremachine, etc.). Another type of data may include the unknown load datacalculated using the measured data.

Once the measured forces are measured, and the unmeasured forces arecalculated, methods and systems consistent with certain embodiments maygenerate a complete free body diagram of a portion (e.g., one or morecomponents) of a machine or the entire machine. Based on the completefree body diagram and the orientation data associated with the machine,or a component thereof, the strain at any desired point on the machinemay be calculated. In certain embodiments where technology is used toobtain a fast sampling rate of the free body diagram, the calculatedstrain at a given point on the machine may be used to continuouslyupdate a prediction of the remaining fatigue life of a structuresurrounding the given point.

In certain embodiments, the data used in determining the remainingfatigue life of a machine may be used for other purposes. For example,the data associated with the complete free body diagram, and other datareflecting the orientation and movement of the machine, may be used toclassify an operation of the machine at any given point in time into oneof several discrete operating states. For example, the data may be usedto determine whether the machine is digging or roading at any givenpoint in time. As another example, the data of the free body diagram maybe used to compute the weight of material in a bucket of the machinefollowing a digging operation. As another example, the data of the freebody diagram and the orientation data, along with information reflectingthe current position of the machine's center of gravity at a given pointin time or operation may be used to determined whether the machine is indanger of tipping. In another example, the data of the free body diagrammay be used to determine historically high loading states of themachine, or individual components thereof, that are experienced duringactual operation. The historical high loading state data may be used tobetter understand the forces experienced by the machine, or componentsthereof, to assist in the design or manufacture stages associated withthe machine. It should be noted that the above examples are not intendedto be limiting, as there are many uses for the data collected andcalculated by the methods and systems disclosed herein.

FIG. 1 illustrates a machine 10. Machine 10 may include a frame 12,power source 14, a work implement 16, an operator station 18, and amonitoring system 20.

Although machine 10 is shown as a vehicle, machine 10 could be any typeof mobile or stationary machine. In the case of a mobile machine,machine 10 may include one or more traction devices 22. Traction devices22 may be any type of traction devices, such as, for example, wheels, asshown in FIG. 1, tracks, belts, or any combinations thereof.

Power source 14 may be mounted to frame 12 and may include any kind ofpower source. For example, power source 14 may be an internal combustionengine, such as a gasoline engine, a diesel engine, a gaseous-fueldriven engine or any other exhaust gas producing engine. Alternatively,power source 14 can be an electric motor, or any other kind of powersource.

Work implement 16 may include any type of implement or tool configuredto facilitate completion of one or more tasks. For example, workimplement 16 may include a construction work tool, such as a loaderbucket, as illustrated in FIG. 1. Other possible construction work toolsmay include blades, drill bits, jackhammers, grapples, etc. Workimplement 16 may also include other types of tools such as clamps,presses, etc.

System 20 may include a structural component 23 of machine 10.Structural component 23 may be configured to undergo mechanical loading.Structural component 23 may be any structural member of machine 10, suchas, for example, frame 12, work implement 16, a support structure forwork implement 16 (e.g., a lift arm 24, a tilt link 25, or a tilt lever26), or any other structural member that may be subjected to mechanicalloading.

Machine 10 may also include various other components associated withwork implement 16. For example, work implement 16 may be powered andcontrolled by a number of actuators, including a tilt actuator 27 and alift actuator (not shown).

FIG. 2 illustrates monitoring system 20 in greater detail. System 20 mayinclude at least one wireless node 28 attached to structural component23. Node 28 may include a strain sensing device 30 configured to measurestrain experienced by structural component 23 at the location of node28.

Strain sensing device 30 may be configured to measure any type ofstrain. For example, strain sensing device 30 may be configured tomeasure axial strain, shear strain, torsional strain, or evenmulti-axial strain (e.g., using a rosette type device).

In some embodiments, strain sensing device 30 may utilize thin filmsattached to structural component 23 to detect strain. The resistance ofsuch thin films may be altered with mechanical strain. Therefore, strainsensing devices that employ such thin films may measure strain bydetecting a change in resistance of the thin film.

In other embodiments, node 28 may be configured to operate using powerharvested from the mechanical loading that it is designed to detect. Forexample, strain sensing device 30 may utilize a piezoelectric transducer(PZT) material. PZT materials may generate electrical power uponexperiencing strain. Therefore strain sensing devices that employ PZTmaterials may be able to generate power as opposed to thin film-basedstrain sensing devices, which consume power in order to detect thechanging resistance of the film. By generating its own power, strainsensing device 30 may be configured to operate substantially orcompletely independent of an external power source. Such independencemay facilitate wireless operation of node 28 as it may not need to becoupled to an external power source. In addition, node 28 may generateenough power with strain sensing device 30 to power other devices atnode 28, such as a processor 32 and/or a wireless transmission device34, both of which will be discussed in more detail below.

FIG. 2 is a block diagram representation of system 20. FIG. 2illustrates an exemplary embodiment including a processor at node 28.Processor 32 may be configured to predict, based on strain measurementstaken by strain sensing device 30, fatigue life of structural component23. Processor 32 may be mounted at any location on or in the vicinity ofstrain sensing device 30 (e.g., mounted to structural component 23,attached to a component adjacent to structural component 23, etc.).

Processor 32 may include any means for receiving information regardingdata collected at node 28 and/or for monitoring, recording, storing,indexing, processing, and/or communicating such information. These meansmay include components such as, for example, a memory, one or more datastorage devices, a central processing unit, or any other components thatmay be used to run an application. Exemplary data that may be stored atnode 28 may include accumulated fatigue damage, total time spent inactive data acquisition, and a histogram of strain ranges.

Although aspects of the present disclosure may be described generally asbeing stored in memory, one skilled in the art will appreciate thatthese aspects can be stored on or read from types of computer programproducts or computer-readable media, such as computer chips andsecondary storage devices.

Node 28 may be configured to wirelessly transmit, to a location remotefrom node 28, information relating to the strain measurements taken bystrain sensing device 30. Wireless transmission device 34 may include,for example, an RF module and/or a transceiver (not shown), and thus,may facilitate such transmission. The location to which node 28 isconfigured to wirelessly transmit may include a central processor 36configured to receive information relating to the strain measurements.Such information may include raw resistance or strain data, calculatedloads from the strain data, selected information like averages or peakvalues, or processed data like fatigue predictions or fatigue damageevaluations. Although data processing may take place at node 28 viaprocessor 32, such processed data may be transmitted to centralprocessor 36 for further processing, sorting, and/or storage. Wirelesstransmission may be accomplished using any type of wireless datalink orother wireless transmission medium. Central processor 36 may be locatedon board machine 10 (as shown in FIG. 2) or, alternatively, at alocation remote from machine 10.

Such information may also be retrieved from node 28 through a physicalinterface. For example, in some embodiments, node 28 may be configuredto interface with a portable electronic device 38. Node 28 may beconfigured to transfer, to portable electronic device 38, informationrelating to the strain measurements. Portable electronic device 38 mayinclude any type of device configured to store and/or process data, suchas a laptop computer, personal digital assistant (PDA), or any otherdevice suitable for downloading or otherwise retrieving such data.System 20 may be configured to interface with any available portableelectronic device and/or, system 20 may include a custom portableelectronic device designed specifically for such an application.

In some embodiments, system 20 may include more than one node 28, asillustrated in FIGS. 2-4. Each node 28 may including a strain sensingdevice 30 configured to take strain measurements. In some embodimentsmore than one structural component of machine 10 may be equipped with anode. Alternatively or additionally, more than one node may be attachedto a single structural component, as shown in FIG. 2 and FIG. 3. Incertain embodiments including more than one node, central processor 36may be configured to receive information relating to the strainmeasurements from more than one of the nodes. Alternatively, oradditionally, nodes 28 may be configured to communicate with one another(i.e., wirelessly).

System 20 may include a display 40. Display 40 may be located at anysuitable location on machine 10, such as, for example, in operatorstation 18. In some embodiments, display 40 may be configured tocommunicate with central processor 36. Display 40 may be any kind ofdisplay, including screen displays, such as, for example, cathode raytubes (CRTs), liquid crystal displays (LCDs), plasma screens, and thelike. Display 40 may be configured to display information regardingsystem 20.

In one embodiment, display 40 may include a warning indicator 42 (e.g.,a warning lamp, warning message, etc.). Warning indicator 42 may beconfigured to activate in response to certain results of the fatigueevaluation performed by system 20. For example, warning indicator 42 maybe configured to activate in response to certain predetermined types offatigue damage results (e.g., indications of cracking, multiple peakloads above predetermined threshold values, etc.) and/or predictedfatigue life (e.g., predicted residual fatigue life shorter than apredetermined threshold value). Warning indicator 42 may be configuredto suggest a course of action in response to such fatigue evaluationresults. For example, warning indicator 42 may be configured to suggestservice, repair, or replacement of certain structural components.

In addition to providing visual feedback regarding fatigue evaluationsperformed by system 20, display 40 may also be configured to displayother information regarding system 20 or any other device and/or systemassociated with machine 10. As an alternative or in addition to display40, system 20 may include one or more audible alerts for conveyinginformation about system 20 to an operator.

Exemplary Monitoring System

FIG. 4 shows an exemplary monitoring system 200 consistent with certaindisclosed embodiments. As shown in FIG. 4, monitoring system 200 issomewhat more comprehensive than monitoring system 20 illustrated inFIG. 2. However, the components and configurations included inmonitoring system 20 may also be included in monitoring system 200. Forexample, monitoring system 200 is shown to include a number of wirelessnodes, which will be discussed in greater detail below. In certainembodiments, these nodes may include the same or similar componentry aswireless nodes 28 illustrated in FIG. 2 (e.g., a strain sending device,a processor, a wireless transmission device, etc.).

In one embodiment, monitoring system 200 may be implemented on a machinethat has moving parts, a rapidly changing load state, etc., such asmachine 10. Further, monitoring system 200 may be configured to performhealth and usage monitoring functions associated with the operations ofmachine 10. That is, monitoring system 200 may be configured to processinformation affiliated with the dynamic load changes experienced bymachine 10. Further, monitoring system 200 may be configured withhardware and/or software that enables it to process machine-related datain real time, as well as generate, store, and manage information relatedto raw data obtained from one or more machine components, such assensors. In this regard, the monitoring system may maintain a manageableset of information for analysis and reporting. Moreover, monitoringsystem 200 may include wireless communication elements (e.g., wirelesstransmission devices 34) that enable moving and non-moving components ofmachine 10 to communicate without wired data links. Other aspects may beimplemented by the disclosed embodiments and the configuration ofmonitoring system 200 is not limited to the examples listed above ordescribed below.

Sensor Network

As shown in FIG. 4, system 200 may include a wired sensor network 202, awireless sensor network 204, a central computer 206 (which may be adigital signal processor (DSP)), and a memory component, such as avehicle database 208. Wired sensor network 202 and wireless sensornetwork 204, together may include sensors for detecting, for example,hydraulic pressures in actuators, positions of cylinder rods, implementlinkage angles, velocities and accelerations, steering articulationangle, strain on bolts forming structural joints, vehicle ground speed,inclination relative to the Earth, and forces on instrumented pins inlinkages and other structures. Data obtained by wired sensor network 202and wireless sensor network 204 may be used to perform structural healthand usage monitoring.

The sensor networks 202 and 204 may each be configured to collect dataindicative of loads acting on machine 10. Although FIG. 4 shows a wiredsensor network 202 and wireless sensor network 204, either network maybe implemented as a wireless or wired network. In one example, wiredsensor network 202 may include an orientation sensor 210, one or morehydraulic pressure sensors 212, one or more cylinder position sensors214, one or more work implement position sensors 216, one or more workimplement velocity sensors 218, load pins 220, and bending bridges 222.Generally, these may all be referred to as “sensors.” In addition, wiredsensor network 202 may include interface electronics 224 and/or anelectronic control module (ECM) 226. In other embodiments, wired sensornetwork 202 of the exemplary health and usage monitoring system 200 mayinclude additional sensors and/or different sensors or other components.

In general, the sensors implemented by machine 10 (e.g., sensors210-228) may be separated into three categories: sensors that senseorientation and movement of the machine, sensors that measure loads(e.g., cylinder pressure sensors, strain gauges on the rod ends ofhydraulic cylinders, etc.), and sensors that sense strain at some point,such as a sensor on a structural frame within machine 10. The number andposition of the sensors implemented within machine 10 may depend on thetype of machine, the type of component(s) within machine, the desiredand actual use of the machine, and other factors. For example, a certainnumber of sensors associated with the first two categories may beselectively positioned in order to provide adequate information toconstrain the problem of generating the entire free body diagram of themachine or machine component. The sensors from the third group, however,may be positioned in locations to provide a base set of measured data tocompare to calculated strains (e.g., normal strain values). Further,based on the location of certain machine components, or other sensors, asensor positioned on these certain machine components may be wired orwireless.

Orientation sensor 210 may be one or more inclinometers disposed onmachine 10 to measure one or both of pitch and roll of machine 10relative to the Earth. Hydraulic pressure sensors 212 may be associatedwith a hydraulic system to detect fluid pressure. In one exemplaryembodiment, pressure sensors 212 may be associated with a cylinder headof tilt actuator 27. Hydraulic pressure sensors 212 may be disposed atother locations about machine 10 to measure hydraulic pressures.Pressure sensors 212 may provide information regarding one or moreforces acting on the structure of machine 10 at connection points of thehydraulic actuator.

Cylinder position sensors 214 may be configured to sense the movementand relative position of one or more components of machine 10. Positionsensors 214 may be operatively coupled, for example, to actuators, suchas tilt actuator 27. Alternatively, position sensors 214 may beoperatively coupled to the joints connecting the various components ofmachine 10. Some examples of suitable position sensors 214 include,among others, length potentiometers, radio frequency resonance sensors,rotary potentiometers, machine articulation angle sensors and the like.

Work implement position sensors 216 may be associated with workimplement 16 in a manner to detect position of work implement 16. In oneexemplary embodiment, work implement position sensors 216 may be rotaryposition sensors disposed at pin connections on work implement 16. Otherposition sensors also may be used including, among others, radiofrequency resonance sensors, rotary potentiometers, angle positionsensors, and the like. Work implement acceleration sensors 218 mayinclude an accelerometer or other type of sensor or sensors configuredto monitor acceleration and may be associated with work implement 16 ina manner to properly detect acceleration of any desired point.Velocities may also be obtained based on the time-derivative of positionsensors for the bucket or other similar component of machine 10.

Load pins 220 may be configured to measure force in x and y-axes ininner and outer shear planes of a pin and may be instrumented with, forexample, one or more strain gauges. The load pins 220 could beinstrumented with strain gauges on the outer or inner surface of thepin, or they could be instrumented with some other technology designedto react to the stress state in the pin, such as magnetostriction. Loadpins 220 may be disposed at joints on machine 10. In one exemplaryembodiment, load pins 220 may be disposed at joints connectingcomponents of work implement 16 and/or connecting the actuators, such astilt actuator 27, to work implement 16. Load pins 220 may be disposed atother joints about machine 10.

Bending bridges 222 may be configured to measure strain in or alongsurfaces, such as, for example, along sides of lift arm 24. In oneexemplary embodiment, the bending bridges may include, for example, fourstrain gauges. In one exemplary embodiment, the strain gauges on bendingbridges 222 may be configured to provide one combined output.

Interface electronics 224 may be in communication with the sensors, suchas load pins 220 and bending bridges 222, and may be configured toreceive data signals from the sensors, process the data signals, andcommunicate data to computer 206. Interface electronics 224 may include,for example, a module including, for example, a PIC 18F 258microprocessor, 32 KB Flash, 1.5 KB RAM, 256 bytes EEPROM, CAN 2.0Binterface with 12 bit external A/D sampling, and 4 strain channels. Inone exemplary embodiment, monitoring system 200 may include nineinterface electronics 224, each associated with load pins 220 andbending bridges 222. The interface electronics may be configured tocommunicate time-stamped and synchronized information, along with sensedvalues.

Electronic control module (ECM) 226 may contain a processor and a memorydevice, and may be configured to receive data signals from sensors 210,212, 214, 216, 218, process the data signals, and communicate data tocomputer 206. The processor in ECM 226 may be a microprocessor or otherprocessor, and may be configured to execute computer readable code orcomputer programming to perform functions, as is known in the art. Thememory device in the ECM 226 may be in communication with the processor,and may provide storage of computer programs and executable code,including algorithms and data enabling processing of the data receivedfrom sensors 210, 212, 214, 216, 218. In one exemplary embodiment, ECM226 may include a MPC555 microprocessor, 2 MB ROM, 256 KB RAM, and 32 KBEEPROM.

Wireless sensor network 204 may include a number of wireless nodes 228and a gateway node 230. Wireless nodes 228 may be disposed about machine10 and may be configured to communicate data signals representative ofmeasured strain to other wireless nodes, and ultimately to gateway node230. Strain gauges 232 may be configured to measure local strain on acomponent of machine 10. Data collected at any of wireless nodes 228 maybe communicated to other wireless nodes 228 and/or to gateway node 230using, for example, a transceiver.

It should be noted that wireless node 28 and wireless node 228 areexemplary only, and may be configured in any known manner. In oneexemplary embodiment, wireless node 228 may include a receiver or atransmitter instead of transceiver. In another exemplary embodiment,wireless nodes 228 may each include a processor and memory forprocessing signals from strain gauges 232. Wireless nodes 228 mayinclude other components, including a power source, such as a battery.Other configurations would be apparent to one skilled in the art.

In one exemplary embodiment, measurements from wireless nodes 228 may betagged with timestamps to allow computer 206 to synchronize themeasurements from the different nodes and from the wired sensor network202. In one exemplary embodiment, wireless nodes 228 may concatenatemeasurements over relatively long periods before data transmission.Means for synchronizing the measurements may be incorporated into thetransmitted data.

Central computer 206 may be located on board machine 10 or,alternatively, at a location remote from machine 10. In embodimentsincluding a central processor, such as central computer 206, data thathas been processed at one or more of wireless nodes 228 may betransmitted to central computer 206 for further processing, sorting,and/or storage. In some embodiments, particularly those without acentral processor, data processing may take place one or more ofwireless nodes 228. It should be understood that any of the functionsfor which central computer 206 may be configured, as discussed herein,may be performed by processors at one or more of wireless nodes 28 orwireless nodes 228. As with wireless nodes 28, wireless transmissionfrom wireless nodes 228 may be accomplished using any type of wirelessdatalink or other wireless transmission medium.

To minimize power usage, wireless nodes 228 may compress and accumulatetheir data and then send the accumulated data periodically, over aprogrammable time interval. In one exemplary embodiment, wireless nodes228 are programmed to send their accumulated strain data over two secondintervals. This means that computer 206, in addition to scaling thesensor data, may rebuild the time history of the actual strains.

The gateway node 230 may be in communication with wireless nodes 228 andmay be in communication with computer 206. Accordingly, the gateway node230 may be configured to communicate data indicative of the straincollected by wireless nodes 228 to computer 206.

It should be noted that monitoring system 200 may also be operable witha single wired network or a single wireless network, rather thansimultaneously employing a wired and a wireless network. Further, thenumber of gauges and other instruments used to collect data may varydepending upon the application and type of machine 10.

Computer 206 may be in communication with gateway node 230, ECM 226, andinterface electronics 224. Computer 206 may be configured to receivedata signals, process the data signals, and communicate data to vehicledatabase 208. Computer 206 may include one or more processors configuredto execute computer readable code that perform processes consistent withcertain disclosed embodiments, such as functions to determine the lifeof, or load on, one or more components of machine 10. In one exemplaryembodiment, computer 206 may be associated with a data transfer device(not shown) that may provide output of data from computer 206 and/orvehicle database 208. The data transfer device could be a portconnectable to a service tool, such as portable electronic device 38.Computer 206 may include, for example, resources to process varyingnumbers of inputs. For instance, computer 206 may execute program codethat stores data in a first-in-first-out buffer at maximum expectedinput sampling rates. Additionally, computer 206 may be configured toperform algorithms consistent with the health and usage monitoringembodiments disclosed herein, such as processing data through one ormore neural networks, performing floating-point matrix calculations,etc. In one exemplary embodiment, computer 206 may be an MPC5200 typeprocessor using a QNX real-time operating system.

Vehicle database 208 may include one or more memory devices that storedata and computer programs and/or executable code, including algorithmsand data enabling processing of the data received from gateway node 230,ECM 226, and interface electronics 224. The memory devices may be anytype of memory device(s) known in the art that is compatible withcomputer 206. Vehicle database 208 also may be configured to store datacalculated by computer 206 and may be configured to store computerprograms and other information accessible by computer 206.

In one embodiment, database 208 may store neural network software that,when executed by computer 206, performs neural network processesconsistent with the disclosed embodiments. A neural network is designedto mimic the operations of the human brain by determining theinteraction between input and response variables based on a network ofprocessing cells. The cells, commonly known as neurons or nodes, aregenerally arranged in layers, with each cell receiving inputs from apreceding layer and providing an output to a subsequent layer. Theinterconnections or links that transfer the inputs and outputs in aneural network are associated with a weight value that may be adjustedto allow the network to produce a predicted output value. Neuralnetworks may provide predicted response values based on historical dataassociated with modeled data provided as independent input variables tothe network. Neural networks may be trained by adjusting the data valuesassociated with the weights of the network each time the historical datais provided as an input to allow the network to accurately predict theoutput variables. To do so, the predicted outputs are compared to actualresponse data of the system and weights are adjusted accordingly until atarget response value is obtained.

Overview Process

As explained, methods and systems consistent with the disclosedembodiments collect, determine, and analyze data associated with forcesexperienced by machine 10. Based on the data, embodiments may calculateunknown variables, such as unknown loads, classify machine operations,generate free body diagrams, calculate strains, determine the life ofcomponent(s) of machine 10, and evaluate and predict the life ofcomponent(s) of machine 10 or of machine 10 itself. FIG. 5 shows aflowchart summarizing some exemplary processes that may be performed bymethods and systems consistent with certain disclosed embodiments.

Initially, in one embodiment, machine 10 may experience a startup stagethat may include powering up machine 10 or otherwise activatingmonitoring system 200. At startup, computer 206 may send one or moresignals to “wake up” wireless nodes 228. Sending the signal may includeactivating or commanding an intermediary component to route the wake upsignal to other connected components. For example, computer 206 mayissue an activation signal to gateway node 230 or a first wireless node228, which in turn notifies one or more remaining wireless nodes 228 toawake and begin processing. At the same time, or at a different time,computer 206 may communicate a signal to one or more wired sensors210-222 in a similar manner as described above, to begin collecting andprocessing inputs.

Once awakened, one or more of sensors 210, 212, 214, 216, 218, 220, 222,228 (hereinafter collectively referred to as “sensors 210-228”) maycollect raw measured data from their associated components (Step 405).For example, sensors 210-228 may be disposed in different locations onmachine 10 to measure one or more parameters of components such as workimplement 16. Once obtained, the raw measured data may be time-stampedand communicated to computer 206 for subsequent processing.Alternatively, in embodiments without a central processor, dataprocessing may occur at the nodes where the data was acquired, atanother wireless node, and/or at a group of wireless nodes.

In one embodiment, computer 206 may determine whether the data receivedfrom each sensor is reliable. In one exemplary embodiment, computer 206may determine data reliability executing neural network softwareconfigured to recognize reliable and unreliable data for each of thetypes of data received from sensors 210-228. For example, the neuralnetwork executed by computer 206 may receive as input data from a sensorshowing a flatline channel. Based on predetermined configurations, theneural network may recognize the flatline channel as an indicator thatthe particular sensor has ceased functioning properly, and therefore,its data may be unreliable. In another example, the neural network mayreceive sensor data that is determined to include a certain threshold ofreliable data (e.g., a majority of data). For example, the neuralnetwork may recognize that a majority of a sensor's data is reliable,but also includes an occasional outlier of incorrect data. The neuralnetwork may recognize this outlier as unreliable data. Therefore, basedon an analysis of the data itself, the neural network may classify thedata as either reliable or unreliable.

In other exemplary embodiments, computer 206 may use other processes todetermine whether the data is reliable. For example, in one exemplaryembodiment, computer 206 may perform a logic-based process that monitorsthe first and second time-derivatives of the incoming data signals. Inthis embodiment, the flatline channel may have a zero first derivativeat all times, and therefore, may be identified as unreliable. Inaddition, an otherwise correctly functioning channel with an occasionalnoise spike may have unusually large higher derivatives at the timesassociated with the noise spike. Therefore, again, the noise spike maybe identified as unreliable.

If computer 206 determines the raw measured data to be unreliable, theunreliable data may be discarded and substituted with a data determinedusing interpolation techniques known in the art (Step 410).Alternatively, when the unreliable data is a strain measurement for aparticular component of machine 10, the unreliable strain measurementmay be substituted with a previously calculated strain for thatcomponent. In some exemplary embodiments, the unreliable data isdiscarded and the strain calculation process continues withoutsubstituting calculated data. In other exemplary embodiments, theprocessing iteration is suspended due to the unreliable data, andcomputer 206 resumes the strain calculation process at step 405. Inother exemplary embodiments, other components of machine 10 may screenthe measured data for reliability, such as gateway node 230, ECM 226,electronics 224, and/or at any of sensors 210-228. In these embodiments,the gateway node 230, the ECM 226, the electronics 224, and/or thesensors 210-228 may be configured with hardware and/or software that iscapable of detecting unreliable measured data.

Also, in step 410, computer 206 may determine that measured strain datais included in the measured data collected in step 405. In certainembodiments, computer 206 may determine measured strain data based oncollected measured data. For example, based on sensor data collectedfrom sensors positioned on opposing sides of a machine component,computer 206 may determine the strain imposed on the component, or aportion thereof. Thus, in step 410, computer 206 may produce measureddata of a first category (i.e., raw measured data reflecting forces on aparticular component, such as pressure, etc.) and measured data of thesecond category (e.g., measured strain data).

In one embodiment, based on the measured data provided in Step 410,computer 206 may determine the load(s) on various structural bodieswithin machine 10 (e.g., one or more components of machine 10) (Step415). Computer 206 may use the load data to determine the strain andfatigue life associated with one or more monitored components of machine10, as described further below. In calculating the load(s), computer 206may convert the measured strain into a proportional quantity thatreflects information that is more relevant to the actual physical strainvalues on the measured component than the measured strain data providedin step 410. For example, computer 206 may convert the measured rawstrain data associated with an instrumented pin located on a machinecomponent into data representing the resultant load and local momentsfor the pin. In another example, computer 206 may convert measured axialstrain data for a cylinder rod to load-based data, which may be thenshifted in order to match load data calculated from head-end and rod-endpressure readings associated with the cylinder rod. In this example, astrain gauge may measure strain data for the cylinder. The strain gaugemay provide the measured strain data to computer 206 for determining theload applied to the cylinder when it is “bottomed-out” (i.e., when a rodwithin the cylinder is fully extended, thus forcing the piston to theedge of one end of the cylinder). In certain embodiments, computer 206may shift received strain data to a corrected value. Because straingauges may sense only strain relative to the time when the gauge wasactivated (i.e., turned on and operational), computer 206 may executesoftware that performs a linear regression analysis, or similar type ofanalysis, on the strain data to create a best-fit expression that isused to offset the loads calculated from the rod strain so that they arein agreement with loads calculated from cylinder pressures when thecylinder is not at either of the extreme limits of displacement. Thelinear regression techniques performed by computer 206 may be thosetechniques known in the art.

Also, in addition to cylinder forces, strain gauges may be implementedwithin machine 10 that measure the axial force in, for example, tiltlink 25. In this example, computer 206 may execute software thatconverts the measured strain data obtained from the strain gauge fortilt link 25 to load-based data using strain to force conversion methodsknown in the art. In instances where the strain gauge does not measureabsolute strain data values, computer 206 may perform correctionprocesses that correct the measured load on tilt link 25 to represent anabsolute strain value. Computer 206 may then use linear regressionanalysis, or similar processes, to determine the axial load in tilt link25.

In certain embodiments, tilt link 25 may experience a dump stop eventduring operation of machine 10. A dump stop event is a condition whentilt link 25 impacts lift arm 24 during operation. The forces imposed onthese elements during such an event may cause fluctuations indetermining the load associated with tilt link 25. As such, computer 206may execute processes that compensate for the forces occurring during adump stop event to accurately determine the load experienced by tiltlink 25. One process may be configured to provide an estimate of theload of tilt link 25 for non-dump stop event states (e.g., when tiltlever 26 is not in contact with lift arm 24). A second process may beconfigured to calculate tilt link load during times when tilt lever 26is in contact with lift arm 24. Each of these processes may be based onload determining algorithms and techniques known in the art and executedby computer 206.

In certain embodiments, one or more wireless strain gauges may beemployed to measure the load on a given component of machine 10. Forinstance, one or more of wireless nodes 228 may be configured as awireless strain gauge for tilt lever 26 that measures its tilt linkload. It should be noted that such determinations may be performed usingwell-known kinematic equations. Further, to conserve the energy ofwireless node 228, each node may be configured with a “sleep” mode. Forinstance, a wireless tilt lever strain gauge, and its accompanyingwireless node 228, my be placed in a low power mode (i.e., “sleep” mode)whenever tilt lever 26 is not in contact with lift arm 24. In anotherexemplary embodiment, computer 206 may estimate the load on a givenmachine component using a neural network configured to provide theoutput load based on known neural network programming softwareprocesses. It should be noted that component loads may be determinedusing other configurations and techniques, and the disclosed embodimentsare not limited to the above described examples.

Computer 206 may also determine unknown loads (e.g., unmeasured loads)acting on or within machine 10, such as all unknown loads for the entiremachine, or certain portions of machine 10, such as a front section,lift arms, etc. For example, unknown loads may be associated with groundinterface loads that cannot be measured directly by a sensor. In certainembodiments, computer 206 may determine unknown loads using a neuralnetwork or by employing traditional deterministic software based uponthe equations of motion. Embodiments involving a neural network aredescribed further below. In some applications, it may not be necessaryto employ a neural network to determine the unknown loads. In certainembodiments, if inertial loading and contributions from mechanicalvibration are not significant, computer 206 may use the Newtonianequations of static equilibrium to determine the unknown loads.

Once computer 206 determines the unknown loads, it may execute softwarethat coordinates system transforms to associate all of the determinedloads (known and unknown) to coordinate data corresponding to one ormore respective components of machine 10 (Step 425). This process allowscomputer 206 to generate a free-body diagram of one or more, or all ofthe respective components of machine 10, which is described in furtherdetail below in connection with FIG. 6.

As explained, the data collected and calculated by monitoring system 200may be used to perform one or more processes consistent with certaindisclosed embodiments. In one embodiment, the determined load data maybe used to calculate strains experienced by one or more components ofmachine 10. For instance, in one embodiment, computer 206 may obtain aninfluence coefficient matrix (A) that is stored in a memory devicewithin machine 10 (Step 430). The influence coefficient matrix (A)includes data reflecting the strain response at a number of chosenlocations of a particular component under the influence of a particularunit load. Using the influence coefficient matrix (A), and otherinformation, computer 206 may calculate strain data associated with oneor more components of machine 10, or for the entire machine itself (Step435.) Details regarding the strain calculations performed by computer206 are further described below in connection with FIG. 7.

In another embodiment, the load(s) determined by computer 206 in Step415 may be used to determine a payload of machine 10 during itsoperations (Step 440). Details of the payload determination processesperformed by computer 206 are further described below in connection withFIG. 10.

In another embodiment, the measured data collected and/or determined bycomputer 206 in Steps 405 and 410, as well as the strain(s) calculatedin Step 435, may be used to determine the fatigue life of one or morecomponents of machine 10, or of machine 10 itself (Step 445). Thisinformation may also be used to determine damage of the one or morecomponents of machine 10, or of machine 10 itself (Step 450).

Also, in another embodiment, the calculations performed by monitoringsystem 200 may produce result data that may be used to update theinformation stored in database 208 (Step 460). For example, informationreflecting the payload determined in Step 440 may be stored in database208 for subsequent processing by computer 206 or an off-board systeminterfaced with machine 200 via communication network (e.g., wireline orwireless network). Further, any damage data for a particularcomponent(s) determined in Step 460, may be stored as information indatabase 208 that is also accessible for subsequent analysis andprocessing. Similarly, the calculated strains and fatigue lifeinformation determined in Steps 435 and 445 may be stored in database208. In this regard, embodiments may continuously update informationreflecting the health and use of one or more components of machine 10,or of machine 10 itself, thus providing up-to date status informationreflecting the operation of machine 10, and its components.

Additionally, the data collected and calculated by monitoring system 200may be used to classify an operation of machine 10 (Step 465). Forexample, the measured and determined data produced by Steps 405 and 410,the unknown loads determined in Step 420, the coordinate systemtransforms determined in Step 425, and the calculated strains obtainedin Step 435, may be used by computer 206 to automatically determine acurrent operation of machine 10. Details regarding classifyingoperations of machine 10 are described below in connection with FIG. 9.

Calculating Unknown Loads to Obtain Free Body Diagram

As explained, methods and systems consistent with certain embodimentsenable monitoring system 200 to calculate unknown loads associated withone or more components of machine 10. In one instance, computer 206 mayuse a neural network to determine unknown loads during operation ofmachine 10. In certain embodiments, the unknown loads may be used togenerate a free-body diagram of a given component of machine 10. FIG. 6illustrates an exemplary unknown load determination process consistentwith certain embodiments.

Initially, the neural network used to determine the unknown loads shouldbe trained. To do so, in one embodiment, a testing process may beperformed before monitoring system 200 performs run-time determinationsof unknown loads. The testing process may be performed for each of afleet of machines, for each type of machine, etc. that is installed withmonitoring system 200. For exemplary purposes, machine 10 is describedas being exposed to the testing process, although it should be notedthat a machine similar to machine 10 may be used in lieu of testingmachine 10 to train the neural network.

During testing, machine 10 may be operated for a predetermined time,under one or more operational conditions. During this time, measureddata is collected (Step 510). The measured data may correspond to aspecific set of measured data, and may be collected via sensors thatmeasure forces, and/or sensors that measure strains representing forcesexperienced by one or more, or all, components of machine 10. Themeasured data may then be used as values in, for example, Newtonianstatic equilibrium equations, that generate output values reflectingunknown loads of specified locations of one or more components ofmachine 10 (Step 520). These output values, along with the specified setof measured data, are fed into a neural network to train the network toprovide predicted unknown loads within a predetermined threshold (e.g.,unknown load values within a certain percentage value of the unknownload values calculated using the Newtonian static equilibrium equations)(Step 530). If the neural network does not produce results within thepredetermined threshold, the weights associated with the network may beadjusted until the network produces unknown load output values that meetthe predetermined threshold criteria.

In one embodiment, the weight values of the network may be defined basedon information determined during previous training of the neuralnetwork. In one embodiment, the neural network may be trained based oncalculated load values associated with areas of a machine component thatare not monitored by sensors during real time operations. Thesecalculations may be performed using a mechanism analysis softwarepackage, such as Pro/Mechanica® Motion, that simulates the operation ofmachine 10 using test data as input. The unknown loads would becalculated as output, and would be used to construct the neural networktraining set.

Once the neural network is trained, its may be stored in a memory devicethat is accessible by computer 206 for execution during operation ofmonitoring system 200. Subsequently, machine 10 may perform operations(Step 540). During these operations, computer 206 collects measured datain a manner similar to the processes described above in connection withSteps 405 and 410 of FIG. 4 (Step 550). The measured data (e.g.,measured force data and strain data reflecting forces on givencomponents) are fed into the neural network, which produces outputvalues reflecting estimates of the unknown loads of machine 10 (Step560).

In one embodiment, once computer 206 determines the unknown loads, itmay also execute software that associates all of the determined loaddata to the coordinate systems corresponding to one or more respectivecomponents of machine 10. This process allows computer 206 to generate afree-body diagram of one or more, or all of the respective components ofmachine 10 (Step 570). FIG. 3 shows one example of lift arm 24 with itsassociated loads (shown as arrows without their respective load datavalues) in a free body form illustration. As shown, lift arm 24 mayinclude twenty-eight externally applied loads. In some embodiments, onlysome of these loads may be directly measured by one or more sensors210-228. The remainder of the loads may be calculated based upon theknown loads in the manner described above in connection with Step 420.Computer 206 may resolve the load data into the appropriate coordinatesystem for any structural component using known algorithms, such astrigonometric calculations, that may vary for each type of machine 10and/or each type of component of machine 10. Alternatively, computer 206may execute neural network software that has been trained to estimateloads in the correct coordinate system of a particular component, suchas lift arm 24. It should be noted that FIG. 3 shows an illustration ofone component of machine 10 including coordinate-based loads. Thedetermined load data, relative to their respective coordinate data, isstored as data in a memory location that may be used to perform otherprocesses consistent with certain disclosed embodiments.

Calculating Strain and Determining Fatigue Life

As explained, computer 206 may execute software that calculates strainsacting on one or more components of machine 10. FIG. 7 shows a flowchartof an exemplary strain calculation process consistent with certaindisclosed embodiments. Initially, computer 206 may retrieve and analyzethe free body diagram(s) previously determined by computer 206, anddescribed above in connection with FIGS. 3 and 6 (Step 710). Dependingon the strains being determined, computer 206 may retrieve and analyzeone or more free body diagrams. For example, to determine all strainsacting upon machine 10, computer 206 may retrieve and analyze the freebody diagram data associated with all components of machine 10.Alternatively, if computer 206 is determining the strain of a particularcomponent, it would retrieve and analyze the free body diagramassociated with that component. In certain embodiments, when determiningthe strains acting on machine 10, computer 206 may process free bodydiagrams one at a time, to later analyze the calculated strains of eachrespective component.

As noted above in connection with FIG. 5, computer 206 may executesoftware to calculate the strains using an influence coefficient matrix(A). As such, computer 206 may retrieve and populate matrix (A)corresponding to the respective component(s) associated with the strainsbeing calculated (Step 720). Each column of matrix (A) may represent thestrain response at a number of chosen locations of a particular machinecomponent (e.g., lift arm 24 shown in FIG. 3) under the influence of aparticular unit load, with all other loads set to zero.

In one embodiment, the influence coefficient matrix (A) may bedetermined by known unit load analysis of a finite element model of thegiven component, as is known in the art. In another exemplaryembodiment, instead of analysis of a finite element model, computer 206may experimentally determine data for selected rows of the influencecoefficient matrix (A) for the given machine component during selectedtime periods of operation of machine 10, such as initial operation timeperiods ranging from start-up of the machine to a certain time periodthereafter (e.g., one or more minutes, hours, etc. later). During thesetime periods of operation, the given component for the matrix underconstruction may be presumed to be structurally sound (e.g., having nofatigue flaws or cracks). Thus, while the component is deemedstructurally sound, the influence coefficient matrix (A) may bedetermined by measuring strains on the component, and performing a leastsquares fit between the known external loads on the component and themeasured strains. Based upon the results of the least squares fitcalculation, the influence coefficient matrix (A) may be populated withthe correct entries. Thus, experimentally determining rows in theinfluence coefficient matrix (A) may be used for high-stress gradientareas in the given component that may be difficult to model using thefinite element method.

The fundamental relationship between measured strain and applied loadfor a quasi-static body is given by the equation below.

$\underset{({r \times 1})}{s} = {\underset{({r \times n})}{A}\underset{({n \times 1})}{f}}$

where r=no. of measured strain channels on a body, and n=no. of externalloads on a body. The influence coefficient matrix (A) may be populatedrow-by-row. In one embodiment, the first row of the above equation maybe written in a time-dependent column vector form, for k time steps intothe future, as shown below.

$\begin{Bmatrix}{s_{1}\left( t_{0} \right)} \\{s_{1}\left( {t_{0} + {\Delta \; t}} \right)} \\{s_{1}\left( {t_{0} + {2\Delta \; t}} \right)} \\\vdots \\{s_{1}\left( {t_{0} + {k\; \Delta \; t}} \right)}\end{Bmatrix} = {\begin{bmatrix}{f^{T}\left( t_{0} \right)} \\{f^{T}\left( {t_{0} + {\Delta \; t}} \right)} \\{f^{T}\left( {t_{0} + {2\Delta \; t}} \right)} \\\vdots \\{f^{T}\left( {t_{0} + {k\; \Delta \; t}} \right)}\end{bmatrix}\begin{Bmatrix}a_{11} \\a_{12} \\a_{13} \\\vdots \\a_{1n}\end{Bmatrix}}$

The least-squares best-fit solution for the unknown elements in thefirst row of the influence coefficient matrix is given by the followingequation.

a ₁ ′=[F ^(T) F] ⁻¹ F ^(T) s ₁

The above calculations may be repeated for the remaining rows of theinfluence coefficient matrix (A). In one embodiment, the number ofiterations (i.e., k) that may be necessary for computer 206 to generatean influence coefficient matrix (A) that is satisfactory for all timeperiods of operation may be dependent on the amount of load data to beobtained. For example, the number of iterations k may continue untiltest data for each of the different loads has been obtained.

Once the influence coefficient matrix (A) has been fully populated,computer 206 calculates the strain using, for example, known matrixmultiplication techniques (Step 730). For example, computer 206 maycalculate the strain by multiplying a column vector including datareflecting the external loads experienced by the given component by theinfluence coefficient matrix (A). The results of the matrixmultiplication representing the calculated strain for the givencomponent may be stored in a memory location for subsequent processing.In one embodiment, computer 206 calculates the strain for each of themonitored components of machine 10 to provide a representation of thestrains experienced by machine 10 during operation.

In accordance with certain embodiments, computer 206 may also executesoftware that performs fatigue life calculation processes to estimatethe life of a given machine component and/or machine 10. The fatiguelife calculation process may accept strain values that are calculatedfrom the multiplication of the external loads on the component by theinfluence coefficient matrix as input, or directly measured strainvalues may serve as input. In this manner, an additional measure ofsystem robustness can be attributed to monitoring system 200.

In certain embodiments, fatigue life calculations may be performed basedon the measured data collected and determined in Steps 405 and 410 ofFIG. 5. Additionally, as noted above, fatigue life calculations may beperformed using the calculated strains determined in Step 435 of FIG. 5,and further described in FIG. 7.

Computer 206 may execute a software process that performs fatigue lifealgorithms to estimate the life of one or more components of machine 10.Computer 206 may calculate the fatigue life of components havingassociated with them one or more strain gauges, such as strain gaugesconfigured in the form of a wireless node 228. This measured straindata, or the strain data calculated in Steps 435 of FIG. 4, may be usedto estimate the accumulated damage in these areas. In one embodiment,estimated fatigue damage may be determined using rainflow analysisfollowed by an application of Miner's rule. Rainflow analysis is amethod to count the cycles in complex, random loading of components.Miner's rule may then be used to sum the resulting damage at each pointof interest of a component. This information, may provide an assessmentof the structure of machine 10. The fatigue damage estimated by Miner'srule effectively provides an estimate of remaining structural life.

For example, to determine the fatigue life of a component, a stress-lifecurve may be used for the welded joints, while a stress-life curve withGoodman mean stress correction may be used for other remainingstructures associated with a component. In addition, separatestrain-life curves may be used as desired for certain locations, such asa strain-life curve with Morrow mean stress calculation. As known in theart, computer 206 may execute software that performs the followingequation to determine fatigue life based on applied stresses.

log N=log a+d·log σ−m·log S

N: number of cycles

a: life intercept; constant for each curve

d: d=0 for B50 curve, d=−1.28 for B10 curve

σ: standard deviation; constant for each curve

m: slope of the curve; constant for each curve

S: stress range=2*Sa (Data)

A Goodman mean stress correction may be conducted using the equationshown below.

${\frac{S_{a}}{S_{a\; 0}} + \frac{S_{m}}{S_{u}}} = 1$

S_(a): stress amplitude with S_(m) (Data)

S_(a0): modified stress amplitude for 0 mean stress

S_(m): mean stress (Data)

S_(u): ultimate strength=material constant

The fatigue damage estimated by Miner's rule effectively provides anestimate of remaining structural life. Fatigue life calculationsutilizing a strain-life approach may be carried out using one of theequations below.

$ɛ_{a} = {\frac{\Delta \; ɛ}{2} = {{\left( {\sigma_{f}^{\prime}/E} \right) \cdot \left( {2N} \right)^{b}} + {ɛ_{f}^{\prime} \cdot \left( {2N} \right)^{c}}}}$

ε_(a): strain amplitude (Data)

Δe: strain range (Data)

N: number of cycles

σ_(f)′: fatigue strength coefficient; material constant

E: Young's modulus; material constant

b: fatigue strength exponent; material constant (negative value)

ε_(f)′: fatigue ductility coefficient; material constant

c: fatigue ductility exponent; material constant (negative value)

Morrow mean stress (S_(m)) correction may be incorporated as in theequation below.

$ɛ_{a} = {\frac{\Delta \; ɛ}{2} = {{\left( {\left( {\sigma_{f}^{\prime} - S_{m}} \right)/E} \right) \cdot \left( {2N} \right)^{b}} + {ɛ_{f}^{\prime} \cdot \left( {2N} \right)^{c}}}}$

The fatigue life may also be calculated from strain obtained by usingthe influence coefficient matrix (A) and the external loads as describedabove. In such embodiments, flags associated with the rows of theinfluence coefficient matrix (A) associated with the location ofinterest of a component may be activated by a user or software processexecuted by computer 206. For example, if an unexpected fatigue problemdevelops at some location that is not equipped with a strain gage, therows of the influence coefficient matrix (A) associated with thatlocation could be activated by altering the value of a flag associatedwith those rows.

The results of the fatigue life calculations may reflect estimatedfatigue life for one or more components of machine 10, as well as anestimate for the fatigue life of the entire structure of machine 10, orportions thereof. Computer 206 may store the fatigue life calculationresults in a memory, such as vehicle database 208, for subsequent accessand analysis.

As the machine 10 continues to perform operations, the present age ofthe machine (designated below as t_(now)) may creep into the probabilitydensity function associated with the distribution of fatigue life, f(t).In one embodiment, computer 206 executes software processes to determinean altered fatigue life distribution for that component based on Bayestheorem. For example, the probability that the fatigue life will be lessthan some arbitrary time in the future, t*, is given by the followingequation.

${p\left( {{life} < t^{*}} \right)} = \frac{\int_{t_{now}}^{t^{*}}{{f(t)}{t}}}{\int_{t_{now}}^{\infty}{{f(t)}{t}}}$

The updated probability density function may then be calculated as inthe following equation.

${f_{up}\left( t^{*} \right)} = \frac{{p\left( {{life} < t^{*}} \right)}}{t^{*}}$

The new expected life (i.e. the mean value of the updated probabilitydensity function) is given by the following equation.

$t_{up\_ mean} = \frac{\int_{t_{now}}^{\infty}{t^{*}{f_{up}\left( t^{*} \right)}\ {t^{*}}}}{\int_{t_{now}}^{\infty}{{f_{up}\left( t^{*} \right)}\ {t^{*}}}}$

Applying the Bayesian approach by computer 206, may avoid user confusionthat may result when the machine hours exceed an original estimated timefor crack initiation with no visible crack present at the componentlocation under analysis. The results of the Bayesian calculation processare also stored in a memory device, such as database 208, for subsequentaccess and use by other processes consistent with the disclosedembodiments.

Damage Detection

In certain embodiments, computer 206 may also be configured to executesoftware that performs a damage detection process for machine 10. Thedamage detection process may include performing a linear regressionanalysis between the calculated strains determined at step 435 of FIG. 5and the measured strain data obtained at step 410. For example, computer206 may perform software processes that generate a function reflectingthe linear regression analysis of the strains calculated in step 435 ofFIG. 5 and the measured strain data determined in step 410 of FIG. 5.The function may be generated based on a graph bounded on the X-axis bythe calculated strains and on the Y-axis by the measured strains. Byanalyzing the slope and/or correlation coefficient of the of the linearregression analysis within this graph, computer 206 may determinewhether damage exists in the component associated with the strains underanalysis. For example, if the slope of the function generated by theregression analysis is not within a certain threshold of “1,” computer206 may determine damage exists in the component. The damage detectedmay be a fatigue crack, a loose bolted joint or other type of failingjoint, and any other form of structural failure associated with acomponent of machine 10.

This embodiment may be further explained based on the influencecoefficient matrix used to calculate strains. In certain embodiments,the influence coefficient matrix (A) is populated early in the machine'slife. Thus, strains calculated during this time frame using matrix (A)may be not be different from the actual measured values. Later inmachine 10's life, the strain values measured and determined bymonitoring system 200 may change values. Thus, when calculating strainsat this stage of machine 10's life, matrix (A) does not accuratelyreflect the strain response of machine 10 at that time. Therefore,comparing the calculated strains with the measured strains using thelinear regression analysis may result in a function having a slopedifferent from “1,” reflecting the difference in strain values betweenthe calculated and measured strains. This difference may reflect acrack, bend, or similar damage to an analyzed component. Computer 206may use rules or other forms of intelligence to determine the level ofdamage based on the difference of the resulting function's slope to thetarget value of slope “1.” For example, the amount of detected damagemay be proportional to the difference in the function's slope from “1.”That is, larger differences between the function's slope from the targetvalue may represent more damage in the analyzed component.

The results of the damage detection process may be stored in a memorydevice, such as database 208, for subsequent access and use by processesconsistent with the disclosed embodiments. In certain embodiments,computer 206 may report the damage to the operator of machine 10 via adisplay device or similar warning indicator. Further, computer 206 maygenerate a damage report and store the information in database 208. Anoff-board system, such as a laptop, server computer, another machine'scomputer, etc., may access database 208 via a communication networkinterconnecting the off-board system and machine 10, such as a wirelineor wireless network. Alternatively, computer 206 may receive a requestfrom an off-board system to send damage reports. In response to therequest, computer 206 may retrieve the damage report stored in database208 and send the report to the requesting off-board system. In anotherembodiment, computer 206 may perform the damage detection process inresponse to the request from the off-board system. Alternatively,computer 206 may perform software processes that automatically directcomputer 206 at periodic times to perform the damage detection processand report the results of the process to predetermine target systems,such as a particular off-board system. It should be noted that thedamage detection results may be accessed and processed by any type ofon-board or off-board system, and the above examples are not intended tobe limiting to the disclosed embodiments.

Operation Classification

As explained, methods and systems consistent with certain embodimentsmay determine the operation being performed by machine 10 based ondifferent types of information. For instance, in certain embodiments,computer 206 may execute software that classifies a current operationbeing performed by machine 10. Classification of the current operationmay include an analysis of the loads acting on machine 10, as well asany other sensed or derived parameters, including, for example,inclination relative to the earth; ground speed velocity and/oracceleration; positions, velocities, and accelerations of the implement;and/or pressures. One method of classifying the operation would be touse traditional deterministic software programming techniques.Alternatively, these parameters may be fed into a neural network foranalysis to classify the current operation. In one example, the neuralnetwork may classify the current operation into one of the followingoperations: (1) roading with no load; (2) digging; (3) roading with aload; (4) dumping; (5) idling; (6) bulldozing; (7) back-dragging; and(8) other. Additional or alternative classifiable operations may beused. Further, while the exemplary operations may be appropriate whenmachine 10 is a wheel loader, they may not be appropriate for adifferent type of machine, such as, for example, a motor grader orhauling machine. Thus, computer 206 may execute software that classifiesoperations that are specific to the type of machine 10, which mayperform the same or different types of classified operations as othertypes of machines.

In embodiments implementing a neural network for operationclassification, database 208 may be installed with a neural network thatis configured to produce output values reflecting certain operationsassociated with the type of machine 10. FIG. 8 shows a flowchart of anexemplary neural network configuration process consistent with certaindisclosed embodiments. To configure the neural network to performoperation classification functions, actual operational data associatedwith the operation classifications are first recorded in a memory deviceduring operation of machine 10 (Step 810). For instance, sensor data maybe collected by computer 206, or another device configured to collectoperational data from machine 10. The recorded data may also includetime stamp information that reflects when particular data values foreach sensor data is obtained during machine operations. The recordedsensor data values (e.g., data values reflecting the measured parameter,such as strain, ground speed velocity, etc.) are designated as inputs,which are assigned to time periods associated with the operation ofmachine 10 during data collection. Thus, each data input may include aset of data inputs arranged as a function of time (e.g., input 1(t1),input 2(t2), . . . , input I (tI), where I may be any positive integer).Based on this information, a user or computer-executed process mayassign an operation classification to each of the time periodsassociated with the input data (Step 820). For example, minimal forcesor strains may be applied to certain components of machine during idletime periods. Accordingly, the user or software process may assign anidle operation classification to the time periods having data inputvalues reflecting these minimal forces or strains.

Once operation classes are assigned to the data inputs for the measuredtime periods, the classified data inputs are fed into a neural networkas inputs in order to train the network to accurately classifyoperations during real time operation of machine 10 (Step 830). Forinstance, in one embodiment, the data inputs are applied to the neuralnetwork to produce, as output data, a predicted set of classifiedoperations for each time period (e.g., time periods 1-I). Further,machine 10 may be exposed to real operations associated with each of theclassified operations. During these operations, computer 206, or anotherinternal or external machine device, may collect actual sensor data.Computer 206, or a testing system, may then compare the predicted outputclassification data values with the actual classification of theoperations performed during the real time operations of machine 10 todetermine whether the neural network predicts the operations of machine10 during each of the time periods within a predetermined criteria. Thepredetermined criteria may be associated with a threshold value thatreflects a maximum acceptable difference between the actual andpredicted classification output values. One skilled in the art wouldrecognize that a number of different conditions, thresholds, etc. may beapplied as the predetermined criteria by the disclosed embodiments. Ifthe neural network does not meet the predetermined criteria, the networkmay be adjusted and re-tested until the predetermined criteria is met.Once the neural network produces accurate predicted classifications, thenetwork may be stored in database 208 for subsequent use in classifyingoperations of machine 10 during later real time operations.

In another embodiment, a process may be implemented that allows a userto classify operations of a machine under test conditions. In thisexemplary embodiment, a machine (e.g., machine 10) may perform one ormore operations over a predetermined period of time. During operation,sensors on the machine may collect measured data associated with one ormore components of the machine. Further, the operation of the machinemay be videotaped or monitored in some form. Subsequently, a user mayview a time stamped video clip of the machine during the recordedoperations and assign operations to certain time periods of theoperation. This time stamped operation data and the collected measureddata is correlated as classification data as a function of time. Theclassification data may be fed as the inputs into the neural network fortraining the network in a manner similar to that described above (e.g.,train the network until the predetermined threshold criteria is met).

In one embodiment, when the neural network does not meet thepredetermined criteria, a user or computer executed process, such asprogram code executed by computer 206, may adjust the weights associatedwith links corresponding to the nodes within the neural network tocompensate for previous inaccurate predictions of operationclassification output values. For example, if the neural networkincludes more than one level of nodes, the weights associated with eachlink interconnecting the layered nodes may be adjusted to train thenetwork to produce more accurate output values. The weight adjustmentsmay be performed by any number of known algorithms used for trainingneural networks, such as algorithms associated with radial basisfunction approximations. One skilled in the art would recognize thatcertain embodiments of the present invention may employ differentalgorithms that affect the learning process of the neural network.

Although the above exemplary embodiment describes the neural networkbeing stored in database 208, the network may also be trained after itis stored in a memory device located in machine 10. Further, the neuralnetwork (trained or untrained) may be stored in a memory device internalto computer 206 or any other electronic component within machine 10. Assuch, embodiments are not limited to the above examples.

Once the neural network is trained and provided in machine 10, computer206 may perform an operation classification process that determines thetype of operation performed by machine 10 during certain time periods ofoperation. FIG. 9 is a flowchart of an exemplary operationclassification process consistent with certain disclosed embodiments. Inaddition to strain data, computer 206, or another component of machine10, may receive sensor data as inputs from sensors 210-228 (Step 910).The received sensor data may reflect operational parameters associatedwith operations of machine 10 over a period of time. Accordingly, thereceived parameter data may be time stamped by sensor 210-228 orcomputer 206. The received parameter data may be checked forreliability, in a manner similar to the processes described below inconnection with steps 420 of FIG. 5.

The received data may then be fed as inputs into the trained neuralnetwork stored in database 208 (or elsewhere) (Step 920). Additionally,computer 206 may feed other information as inputs to the neural network.For example, unknown load data, free body diagram data, and calculatedstrain data may be used as inputs to the network. The neural networkprocesses the inputs using known neural network processes and producesoutput values. Based on the output values, computer 206 may determinethe classification of an operation performed during certain periods oftime of operation of machine 10 (Step 930). For instance, based onparameter data values associated with one or more machine components,computer 206 may determine at time t₁, machine 10 was roading with noload, digging, roading with a load, dumping a load, etc. The operationclassification information may be stored in a memory location within amemory device (e.g., database 208, local memory within computer 206,etc.) for subsequent processing consistent with certain disclosedembodiments.

It should be noted that computer 206 may execute more than one neuralnetwork to perform any of the neural network processes described above.For example, one neural network may be used to classify the currentoperation, while a second neural network may be used to determine theunknown loads of a machine component.

Payload Determination

As described, methods and systems consistent with certain embodimentsenable computer 206 to execute software that estimates the payloadcarried by machine 10 based on the measured data. Some payloaddetermination systems may require that the operator pause the machineand then request an estimate from the system just before dumping theload. Pausing the machine ensured that inertial forces would not corruptthe payload determination. The disclosed embodiments enable payloaddeterminations to take place without pausing the machine during dumpoperations.

FIG. 10 shows a flowchart of an exemplary payload determination process1000 consistent with certain embodiments. At step 1002, measured dataand calculated loads (i.e., the results of the operation classificationprocess described above in connection with FIG. 9) may be received bycomputer 206 as inputs. At a step 1004, based on these inputs, computer206 may determine whether the current operation is classified as acertain type of operation, such as a dump operation. If the currentoperation is not classified as a dump operation (Step 1004; No), thenthe payload determination process returns to step 1002. The payloaddetermination process may continue to loop until the current operationis classified as a dump operation. It should be noted that the dumpoperation is an exemplary operation used by computer 206 during thepayload determination process. The disclosed embodiments contemplateusing other types of classified operations to determine whether tocalculate the payload of machine 10.

When the current operation is classified as a dump operation (Step 1004;Yes), computer 206 may perform a kinematics analysis to determine andconsider the motion of machine 10 and/or one or more of its components,such as work implement 16 (Step 1006). Further, computer 206 may performa kinetics analysis to calculate and determine the payload mass (Step1008). The kinetics analysis may include determining the mass M from aderived expression for the payload, M=f(q_(i)). In the expression forthe payload, time derivatives of measured quantities q appear. These maybe calculated via numerical differentiation using a three-point centraldifference method, shown in the equation below.

${\frac{q}{t}\left( t_{n} \right)} \approx \frac{q_{n + 1} - q_{n - 1}}{2\Delta \; t}$

In the equation, Δt is the time increment between each sampled value.Determining the mass of the load may be performed by solving for thenecessary unmeasured variables, such as, for example, loads atnon-instrumented pins and other unknowns. In one exemplary embodiment,computer 206 may execute software that solves, for example, a 10×10system for ten variables that contribute to determining the payload ofmachine 10. In one exemplary embodiment, the 10×10 system may becombined into a single, derived analytical expression to determine thepayload using methods known in the art. The derived expression for thepayload, M=f(q_(i)) may account for all inertial effects during theloading and dumping process. The expression M=f(q_(i)) is derived from aset of Newtonian equations of motion for the front linkage of machine10. Algebraic manipulations are performed to reduce all of the equationsto a form M=f(q_(i)). The Newtonian equations of motion involvevariables related to inertial effects so that these effects can beaccounted for in the payload calculation. Accordingly, an operator mayno longer need to pause machine 10 to allow the machine to calculate itspayload. Further, computer 206 may be configured with software that,based on the operation classification of machine 10, automaticallydetermines payload at predetermined times, such as when machine is aboutto perform a dump operation, during roading with load, etc.

In one exemplary embodiment, computer 206 may execute software thatdetermines different stages of a dump operation once this operation isclassified. For instance, computer 206 may execute software thatdetermines different stages of a dump operation based on the positionsand load data of one or more components of machine 10 during aclassified dump operation. Accordingly, computer 206 may detect whenmachine 10 is beginning, performing, and ending a dump operation. Basedon this knowledge, computer 206 may perform the above describedkinematics and kinetic analysis processes at both the beginning and endof the determined dump operation. In this regard, computer 206 maydetermine the mass of the delivered material by determining thedifference between the mass of the payload at the beginning of the dumpoperation and at the end of the dumping operation (Step 1010).Therefore, the payload calculation may reflect the mass of deliveredmaterial, even when only a part of the payload is dumped.

In one exemplary embodiment, the determined payload may be optionallydisplayed in an operator interface (e.g., display 40) located inoperator station 18 (Step 1012). This may allow an operator to track theweight of material being dumped by machine 10. In addition to thepayload amount, display 40 may convey additional information to theoperator (e.g., with warning indicator 42). Such information mayinclude, for example, an impending tip-over alert and a maximum loadscenario, both of which may be determined by computer 206 based ondetermined load and strain data, as well as other measured parameters,such as inclination relative to the Earth. Display 40 and/or warningindicator 42 may include, for example, an audible noise, lights, aliquid-crystal display, etc.

Computer 206 may store data reflecting the calculated payload in amemory device, such as vehicle database 208. To this end, computer 206may perform a process that determines a cumulative payload for a giventime period based on previously calculated and stored payloadinformation. The cumulative payload information may be maintained indatabase 208, and displayed by display 40. Such payload information mayalso be downloaded off-board machine 10 for subsequent processing.

In one embodiment, computer 206 may execute neural network software thatis trained to determine payload of machine 10 based on measured stressdata, determined load data, and other collected parameter information.In another embodiment, monitoring system 200 may interface with someother pre-existing payload determination system rather than rely on theprocesses for payload determination described here.

INDUSTRIAL APPLICABILITY

The disclosed monitoring system may be applicable to any structuralcomponent configured to be subjected to mechanical loading. Thedisclosed system may be configured to evaluate fatigue in structuralcomponents of machines. The disclosed system may be applicable tostationary machines, such as power generation sets, cranes, lifts, etc.,as well as mobile machines, such as construction equipment like loaders,track type tractors (e.g., bulldozers), hauling vehicles, excavators,earthmovers, etc. The disclosed system may be applicable to machines ofany size and configured for any task. In some embodiments, the disclosedsystem may be configured to evaluate fatigue in machines having movingparts. In other embodiments, the disclosed system may be used toevaluate fatigue in mechanically loaded structures without moving parts.

An exemplary method of using the disclosed monitoring system may includetaking measurements of strain experienced by structural component 23 atnode 28 attached to structural component 23. The method may furtherinclude processing data collected regarding the strain measurements withprocessor 32 located at node 28. The method may also include predicting,with processor 32, fatigue life of structural component 23 based on thestrain measurements. In addition, the method may include evaluatingfatigue damage based on the strain measurements.

An exemplary method of using the disclosed system may also includetransferring recorded and, in some embodiments, processed data. Forexample, the method may include wirelessly transmitting, to a locationremote from node 28, information relating to the strain measurements. Insome embodiments, the method may include taking strain measurements attwo or more nodes 28, and wirelessly transmitting, to central processor36 information relating to the strain measurements from nodes 28. Inother embodiments, the method may include interfacing node 28 withportable electronic device 38 and transferring, to portable electronicdevice 38, information relating to the strain measurements.

An exemplary method of using the disclosed system may further includeoperating node 28 using power harvested from the mechanical loadingbeing monitored by strain sensing device 30. Such power harvesting maybe accomplished using a piezoelectric transducer material.

Methods and systems consistent with the disclosed embodiments may usefatigue data, including collected strain data, calculated strains,loads, operational information, etc. to provide estimates of fatiguelife and/or fatigue damage for one or more components of machine 10, aswell as payload of machine 10. This information may be used to provideinsight regarding the health of machine 10 and to gather informationuseful for future design improvements of machines. In certainembodiments, the information determined by system 20 may be useful todesign future machines, operate machines, to determine resale valuesbased on known wear of machine 10, and/or to determine when to performmaintenance and/or repair. In addition, system 20 may provide healthinformation that is relevant and useful to a number of entities,including, for example, machine owners, machine operators, machinepurchasers, service mechanics, and machine developers and engineers.Such relevant information may include, 1) fatigue damage data, 2)fatigue life predictions, 3) extreme load cases for one or morecomponents, 4) load histories at various severity levels, 5) damage ratedata, and 6) crack detection.

Information regarding fatigue data may be stored at node 28 and may bemade accessible to one or more users. In certain embodiments, system 20may continuously update the stored data that is representative of thestructural health of the structural component 23 and/or of machine 10.Processor 32 may be configured to utilize the fatigue data to estimatethe residual life and/or value of a particular component, set ofcomponents, or machine. Alternatively or additionally, users may accessthe fatigue data and may perform their own fatigue evaluation based onthe data. Information about fatigue damage and/or predicted fatigue lifemay be relevant to those purchasing and/or selling machines that havebeen previously operated.

In one exemplary embodiment, instead of continuously storing damagerelated data for all locations of each of the monitored components,monitoring system 200 may track accumulated damage only at discretelocations, such as at the locations where one or more sensors areactively sending signals to computer 206. Further, health and usagemonitoring system 200 may optionally accumulate damage data via thecalculated strain at a number of desired component locations, asdetermined by a user. In one exemplary embodiment, the cumulative damagemay be partitioned into portions attributable to each of the variousmachine operations. For example, in a wheel loader machine monitoringsystem may store cumulative damage data in matrix form. Each row of thematrix may correspond to a particular location of a wireless node 228and a data value associated with an amount of damage. The matrix may beconfigured in any form, such as a designated set of columns storingaccumulated damage data for each of the classified operations for thatmachine. A related column may also store the total damage data for aparticular wireless node 228. The damage data may include directionalinformation corresponding to the most likely-orientation of a fatiguecrack.

Information regarding the different classifiable operations may bestored within vehicle database 208 and made available to one or moreusers or computer systems, such as computer 206. The types of operationsthat may be classified by the disclosed embodiments may vary based onthe type of machine to machine. For example, a wheel loader may haveassociated classifiable operations such as, for example, roading with noload, digging, roading with a load, dumping, idling, bulldozing, backdragging, and “other.” The classification of these operations may beperformed either by a neural network or via deterministic software.

Once classified into a specific operation, computer 206 may executesoftware that determines the amount of time spent performing anoperation in real-time. In one example, computer 206 may store thisinformation in vehicle database 208 as data indicative of a total amountof time that machine 10 operates in a particular operation. For example,based on collected sensor data, and determined load and other parameterinformation, computer 206 may determine that machine 10 is entering adigging operation at a time t₁. The operation may continue untilcomputer 206 determines that an operation other than digging is beingperformed. At that time, computer designates the end of the diggingoperation at a time t₂. The time period between t₁ and t₂ may be summedwith the time periods of other digging operations to maintain a totaltime period that machine 10 is operating in a digging operation.Computer 206 may perform software that forms this information in ahistogram. Alternatively, computer 206 may download this information toan off-board system that forms the histogram. The histogram also mayinclude information showing a total operation time for each of the otherclassifiable operations. Alternatively, embodiments may form separatehistograms for the other classified operations, or selected combinationsof operations. Further, the operation information maintained by healthand usage monitoring system 200 may be customized or configured based ondesired characteristics. For example, the total time spent working in aclassifiable operation need not reflect a total time over the lifetimeof machine 10. Instead, the total time data for the classified operationmay reflect the amount of time working in the classifiable operationsince a last maintenance job was performed on the machine, the totalamount of time working in the classifiable operation at a specificworksite, etc.

Computer 206 also may be configured to execute software that determinesin real-time the amount of fatigue of at least one component of machine10 over a period of time due to a specific operation. Again, referringto the digging operation as an example, when determining the fatigue,computer 206 may determine that the machine is entering a diggingoperation at time t₁. The operation may continue until computer 206determines that the digging operation has ended at time t₂. Using anyfatigue life calculation processes described above, computer 206 maydetermine the component's fatigue life by calculating the amount offatigue that occurred during the time period between t₁ to t₂. Summingthe determined fatigue with the total lifetime fatigue that occurredwhile performing the operation may provide a total amount of fatigue dueto the classifiable operation. Further, the total amount of fatigue maybe reflective of fatigue during the machine's entire operationallifetime, since the machine's last maintenance, since initiating work ata specific worksite, etc. This information may be displayed in ahistogram showing the fatigue for each operation separately,collectively, or for sets of selected operations.

In another embodiment, health and usage monitoring system 200 may storeinformation regarding the extreme (e.g., maximum or minimum)instantaneous load scenarios over a machine's or component's lifetime.For example, referring to FIG. 11, a body 1100 is shown under theinfluence of any number of external loads f_(i) (i=1 to n). The body1100 may be a portion of any of the structures comprising machine 10.When any one of the external loads f_(i) complies with a pre-establishedfactor, such as surpassing a previously stored lifetime maximum orminimum load value, then computer 206 may update vehicle database 208with the new lifetime maximum or minimum value. By storing both thelifetime maximum load and the lifetime minimum load, a total range ofloading is captured and available for analysis and display on a displaydevice in any format.

In one exemplary embodiment, health and usage monitoring system 200 maystore a snapshot of all load values acting on body 1100 at a particularpoint in time. For example, when any one load value exceeds apre-established factor of a lifetime maximum or minimum value, computer206 stores data regarding all the applied load values for body 1100. Inone exemplary embodiment, each of the external loads f_(i) may beconsidered an element in a column vector f(t). To save the dataregarding all the applied loads, the complete column vector f(t) may besaved. It should be noted that each body and each load being monitoredby health and usage monitoring system 200 may have its own “extreme loadcase matrix.” The extreme instantaneous load scenario providesinformation regarding the most devastating instantaneous load applied tothe body 1100 for each applied load.

In addition, health and usage monitoring system 200 may associate thelifetime maximum and minimum load values with the machine's currentoperation classification in memory. The load data and correspondingoperation classification information may be used in designing anddeveloping components for avoiding yield or buckling failures. Thus, incertain embodiments, computer 206, or another machine system, may sendthe stored load and operation classification information to an off-boardcomputer system for subsequent analysis, such as design, manufacturing,and diagnostic analysis.

In certain embodiments, computer 206 may execute software that stores indatabase 208 information associated with the load histories at variousseverity levels for each classified operation over one or more selectedtime periods (e.g., t₁ to t₂). This information may be provided to theoperator of machine 10 or to off-board systems for analysis bycomputer-executed software or a user. For example, the storedinformation may include data reflecting the loads applied to a givencomponent determined at time t₁(e.g., the beginning of a classifiedoperation) to time t₂ (e.g., the end of the classifiable operation) fora desired severity level. A desired severity level may be represented asa percentage value reflecting a level of damage experienced by a givencomponent(s). For example, a 100% severity level may reflect the mostdamaging example experienced by the component or machine during a givenoperation. A 50% severity level may represent the average damageexperienced by the component or machine during the operation.Accordingly, for example, a user or software process may direct computer206 to store the load history of the component at the 90th percentileseverity level for a digging operation. Computer 206 collects and storesall the loads that occur during the digging operation when the loadsamount to a severity level at the 90th percentile. The determination asto whether a particular loading history is at a 90^(th) percentileseverity level may be made by comparing the damage rate for thatparticular operation with a known 90^(th) percentile damage rateobtained from the damage rate histogram (contained in database 208)associated with some component of machine 10. In one exemplaryembodiment, when a newly collected load history is associated with aseverity level that is closer in value to a pre-selected severity level(e.g., a desired level set by a user) than that of a previously savedload history associated with the same severity level, the newlycollected load history may replace the previously stored history indatabase 208.

To better illustrate this exemplary embodiment, FIG. 12 shows aflowchart for storing load history information in vehicle database 208.Initially, machine 10 performs an operation. During the currentoperation, computer 206 collects measured data and load data in a mannerconsistent with the process steps described above in connection withFIG. 5 (Step 1205). Computer 206 collects the measured data associatedwith a given component of machine 10 at a time t₁. Recording may bebased on a pre-established first triggering event. In one exemplaryembodiment, the triggering event may be associated with the initiationof a classified operation, as determined in a manner consistent with theabove disclosed embodiments. For instance, referring to the abovementioned digging operation as an example, computer 206 may detect afirst triggering event when it determines machine 10 has begun a diggingoperation.

At some point, computer 206 stops recording load data at apre-established second triggering event at time t₂. The secondtriggering event may be an indication that a classified operation hasended, as determined by computer 206 in a manner consistent with theabove disclosed embodiments.

Computer 206 may also determine the current operation performed bymachine 10 during the monitored time period t₁<t<t₂ (Step 1207). In oneembodiment, computer may execute a neural network to classify thecurrent operation in a manner similar to that described above inconnection with FIG. 9. Additionally, computer 206 may collect load datadetermined by computer 206 in a manner consistent with the disclosedembodiments. The load data may be used to populate a candidate loadmatrix (Step 1208). The candidate load matrix reflects a data structureincluding the load history for the component or machine 10 during thecurrent operation for the given time period t. Thus, the stored loaddata in the matrix may include all the loads applied to the analyzedcomponent over the time period t₁<t<t₂.

Computer 206 may also determine a target damage rate for one or morecomponents of machine 10 for the time period t. The target damage ratecorresponds to the desired severity level percentile. Accordingly, ifthe desired severity level is the 90th percentile for the exemplarydigging operation, computer 206 may select the target damage rate tocorrespond to the 90th percentile damage rate. In certain embodiments,computer 206 may access and analyze a damage rate histogram to determinethe target damage rate. For example, the body 1100 of FIG. 11 is shownunder the influence of any number of external loads f_(i) (i=1 to n).The load history for the i^(th) operation, f(t) may give rise to acorresponding damage field, D_(i)(r,t), which is increasing with time.Accordingly, for each of the different operations performable by machine10, a complete finite time history (of length t₂−t_(i)) of f(t) may beselected by monitoring the residual of the following equation.

$\frac{{\int_{S}{{w(r)}{D_{i}\left( {r,t_{2}} \right)}{A}}} - {\int_{S}{{w(r)}{D_{i}\left( {r,t_{1}} \right)}{A}}}}{t_{2} - t_{1}} = {{target}\mspace{14mu} {damage}\mspace{14mu} {rate}}$

As explained above, the target damage rate is the damage ratecorresponding to the desired percentile of severity. The damage field,D_(i)(r,t), is the amount of damage that exists on the component at timet at a particular surface location r (a position vector in the body'slocal coordinate system) attributable to the i^(th) operation. Aweighting function, w(r), is a measure of the relative importance offailure at various locations on the surface of the body being monitored.For example, a weld failure at one location on a body might be moredamaging, and more expensive to repair, than a similar failure onanother part of the body. The time interval, t₂−t₁, for the load historyf_(i)(t) may be equal to the duration of one instance of that operationthat most closely satisfies the target damage rate equation. In thetarget damage rate equation above, the quantities are shown in integralform in order to conveniently convey the concept. In another exemplaryembodiment, however, the surface integrals may be approximated in theform of a weighted summation. A total damage field, D_(total)(r,t), maybe determined using the following equation.

${D_{total}\left( {r,t} \right)} = {\sum\limits_{i = 1}^{\# \mspace{14mu} {of}\mspace{14mu} {ops}}\; {D_{i}\left( {r,t} \right)}}$

It should be noted that the direction of damage used for the summationin the above equation must be the same for all of the classifiableoperations. Also, it should be noted that an amount of total damage maybe different on every plane. Accordingly, the plane having the highestamount of total damage must be the plane utilized when solving for thetarget damage rate. Again, the location of each wireless node 228 maypotentially have a different plane of maximum damage. The target damagerate may be determined prior to or during the current operation ofmachine 10.

Computer 206 may also calculate the current damage rate for thecomponent over a time period t of the single digging operation, wheret₁<t<t₂ (Step 1210). Computer 206 may determine the total damage usingthe equations for determining the target damage rate described above,based on the measured data associated with the current operation of themachine.

Once the target damage amount is obtained, and the current damage rateis determined, computer 206 may evaluate a residual of the currentdamage rate (Step 1215). A residual represents a degree to which thetarget damage rate (determined above) is not satisfied. In oneembodiment, computer 206 may determine the current residual of thecurrent damage rate by calculating a damage factor residual based uponthe target damage rate. The target damage factor residual reflects adifference between the desired target damage rate (in this example, the90^(th) percentile) and the actual damage rate determined by computer206. It may be expected that some small amount of residual may exist, asany externally applied load history may not exactly give rise to thetarget damage rate (in this example, the 90^(th) percentile).

Once the current residual is determined, computer 206 may collect apreviously stored residual from a memory device within machine 10, suchas database 208 (Step 1220). The previous residual corresponds to aresidual that was previously determined by computer 206 based on aprevious operation similar to the current operation of machine 10,determined in Step 1207. Also, computer 206 may previously havegenerated and stored in database 208 a previous load matrix associatedwith the previous residual.

At step 1225, computer 206 may compare the current residual with thepreviously stored damage residual. Based upon the comparison, computer206 may determine whether the new load history (i.e., candidate loadmatrix) more closely corresponds to the desired target damage rate bydetermining whether the current residual is less than the previousresidual. If the current residual is not less than the previous residual(Step 1225; No), then computer 206 determines the previously storedresidual, with its associated load history, is closer to the targetseverity level (such as the 90th percentile) than the newly calculatedresidual with its load history (i.e., the candidate load matrix).Accordingly, computer 206 flushes the current residual and candidatematrix (1245), and the may return to step 1205 to monitor the nextoperation.

If, however the current residual is less than the previous residual(Step 1225; Yes), computer 206 may determine the newly calculatedresidual with its associated new load history (i.e., candidate loadmatrix), is closer to the target severity level (such as the 90thpercentile) than the previously stored residual with its associated loadhistory (i.e., previously stored load matrix). Accordingly, computer 206replaces the previously stored residual with the current residual, andthe load history associated with the previous residual (i.e., previousload matrix) is replaced with the current load history (i.e., candidateload matrix) in database 208 (Step 1250). In this manner, health andusage monitoring system 200 may continuously monitor for a load historythat most closely matches the desired load severity. The process mayreturn to Step 1205 to monitor for the next operation.

Although in the example described, the desired severity level was the90^(th) percentile, it is contemplated that the disclosed embodimentsmay store the load history for a classified operation at any desiredpercentile. In one example, computer 206 is configured to executesoftware that stores the load histories for each operation at thelifetime maximum (i.e., 100th percentile). Alternatively, computer 206may store load histories for sets of severity levels, such as the 90th,50th, and 10th percentile for each component and each operation. Storingthe load history between time t₁ and t₂ for each operation at a desiredseverity level provides a manageable amount of data for analysis, thusreducing the amount of memory space used to store load history data.Accordingly, a user or software executed process may access database 208to view and/or analyze the load histories for each desired severitylevel and thus obtain operation profiles of machine 10.

Additionally, as mentioned above, information for generating a damagerate histogram may be stored in vehicle database 208 and made accessibleto a user or computer system, such as computer 206 or an off-boardcomputer system. For example, FIG. 13 shows a damage rate histogram 1300for an exemplary operation of machine 10. In this exemplary embodiment,histogram 900 may be developed to represent the damage rate over thelifetime of machine 10 for the single classified operation, such as thedigging operation.

As shown in FIG. 13, the x-axis of histogram 1300 may represent damagerates with increasing severity. The y-axis may represent percentage ofhours spent at each damage rate while performing the respectiveclassified operation. Computer 206, or other system components ofmachine 10, may collect the information for generating the histogram.For example, sensors 210-228 may measure and collect certain types ofdata, such as strain data values. Computer 206 may use the collecteddata to perform the fatigue life calculations described above consistentwith certain disclosed embodiments. Therefore, computer 206 maydetermine the length of time for a single classified operation timeinterval (e.g., t₁<t<t₂). The change in determined damage may be dividedby the time period, and the quotient will determine the damage rates,which computer 206 (or another computer-based system) may format intodata that may be provided to display 40 for display. Computer 206 mayuse the result of the calculated weighted integral damage rate,determined using the target damage equation described above, to updatethe histogram 1300. After the histogram has been populated, a damagerate of any severity level (e.g., 10th, 50th, 90th, or 100th percentile)for any operation may be obtained for use in selecting the load historyof corresponding severity.

In another embodiment, computer 206 may store information in database208 reflecting the detection of a crack or loosely fitted partassociated with a component of machine 10. As explained above, methodsand systems of the disclosed embodiments may detect a crack based on acomparison/cross-plot of calculated and measured strain for thecomponent. For instance, computer 206 may consider the slope andcorrelation coefficient derived from a cross-plot and least-squareslinear fit of calculated vs. measured strain for any given strainchannel of the component. Computer 206 may use this information as anindication of crack initiation in the given component and provide awarning to a user or other computer system. Thus, a user may be warnedof pending cracks in a machine component. For example, computer 206 mayprovide a warning of crack initiation for a given component to theoperator or owner of machine 10, via display 40 and/or warning indicator42. Alternatively, or additionally, computer 206 may provide the warningto a software process executing in another computer system, such as anECM controlling engine operations. In response to the warning, thecomputer system may perform a process to avoid further damage to thecomponent, such as reducing engine idle speed, stopping the engine, etc.

It should be noted that the information contained in vehicle database208 may be used to extrapolate accumulated damage associated with one ormore components of machine 10, or of machine 10 itself, even if one ormore of sensors 210-228 have ceased to function. For example, consider asituation where all of the instrumented pins and wireless strain gagesimplemented on machine 10 have ceased to function, and only vehiclespeed, cylinder displacement, and cylinder force sensors remainoperational. Computer 206 may still be able to determine the classifiedoperation of machine 10 based on the available parameters obtained fromthe operational sensors. For instance, during a particular operation,computer 206 may match the average power expended by the cylinders tothe appropriate “damage-rate bin” in the damage rate histogram 1300 forthat operation, based upon previous correlations made by computer 206.In this manner, computer 206 may estimate an additional amount of damagewithout any fatigue life calculations based upon strain, eithercalculated or measured.

As explained, methods and systems consistent with the disclosedembodiments enable a user or off-board system to collect health andusage information from machine 10. FIG. 14 shows a flow chart 1450 of anexemplary data analysis process consistent with certain embodiments. Inone example, computer 206, or another computer system, may transferinformation from vehicle database 208 to an off-board system, such as anexternal database (Step 1452). In one embodiment, the off-board systemmay be configured to receive and analyze information from a plurality ofmachines.

At step 1454, the off-board system may perform statistical analysis ofthe information. For example, when information from multiple machineshas been downloaded to an off-board database, the off-board system, auser, or another computer system, may access the database to compare andanalyze the machine information for structural integrity of one or morecomponents of machine 10, and the other machines. The information may beanalyzed for, among other things, abusive use of the analyzed machine.Further, the off-board system may rank the remaining fatigue life of oneor more machines (Step 1456).

Steps 1458 to 1468 show a number of possible uses of the informationdownloaded from machine 10. For example, at step 1458, a servicecontract may be priced based on the fatigue life evaluated at step 1456.The service contract may be priced to take into account the remainingfatigue life, as well as any rough handling due to heavy loading thatmay have been applied to machine 10.

At step 1460, the data may be screened for condition-based maintenancethat should be performed. This may include evaluating the health relateddata for machine 10 to determine which components are most in need ofmaintenance based upon their remaining life or their condition.Accordingly, components having a short remaining fatigue life may bemaintained or replaced to ensure efficient and continuous operation ofmachine 10.

At a step 1462, sales forecasts may be created by dealers based upon theremaining fatigue life of the machines. Accordingly, dealers may be ableto predict the future needs of a customer and thereby create salesforecasts. At a step 1464, engineers may design or generate new designsfor machine 10 based upon the evaluated fatigue life and the informationobtained from machine 10. For example, using the information obtainedfrom machine 10, engineers may be able to remove excess material fromcomponents or areas of components that may not receive high stress. Inone exemplary embodiment, new designs may be based upon the downloadedhealth information.

In step 1466, efficiencies of a work site including one or more machinesthat include health and usage monitoring system 200 may be evaluated. Inone exemplary embodiment, an off-board system may review and analyze theamount of time a machine, or a set of machines, perform a specificoperation. For example, if an inordinate amount of time is spent roadingwithout a load, then that efficiency may be noted and corrected tocreate a more efficient work site.

In step 1468, the efficiency of specific operators may be evaluatedusing the information obtained from vehicle database 208. For example,the information may indicate that one operator is more efficient thananother operator in performing certain operations of a type of machineat a work site. Using such information, work site managers may be ableto recommend additional training or additional operators to maintain anefficient work site. Other uses for the information obtained frommonitoring system 200 may also be available.

Although monitoring system 200 is described with reference to a machine,it should be noted that methods and systems consistent with thedisclosed embodiments may be used with structures and components otherthan machines. For example, monitoring system 200 may be used to monitorany structural system subject to fatigue life. In some embodiments,monitoring system 200 may be used to monitor any structural system thatmay be used to perform a plurality of operations. Some examples of otherstructures that may incorporate monitoring system 200 disclosed hereinmay include civil structures, aircraft, and automobiles. Otherstructures may equally benefit from the system described herein.

Methods and systems consistent with the disclosed embodiments provideuseful information that allows operators, user, and computer systems toassess the health of a machine and perform further analysis based onthis information. For instance, because monitoring system 200 gathersdata related to structural mechanics and estimates structural life innear real-time, it may be used to monitor the structural integrity of astructure, such as, for example a machine, throughout the structure'slifespan. Moreover, as mentioned above, monitoring system 200 also maybe useful for providing information to assist the design, manufacture,operation, resale, and repair of components and machines. For example,over time, the information obtained by monitoring system 200 may be usedto more efficiently design and more accurately analyze components of amachine. The structures may then be redesigned and manufactured with,for example, different materials and dimensions that may be lighter,less expensive, stronger, etc., while still providing acceptableperformance in the field.

It will be apparent to those skilled in the art that variousmodifications and variations can be made in the disclosed embodimentswithout departing from the scope of the invention. Other embodiments ofthe disclosed embodiments will be apparent to those skilled in the artfrom consideration of the specification and practice of the disclosedembodiment. For example, the process steps shown in the disclosedfigures may be performed in different order, and are not limited to thesequences illustrated therein. Also, additional or fewer process stepsmay be implemented during these processes. Further, although thedisclosed embodiments describe computer 206 executing softwareassociated with neural networks to perform specific processes, methodsand systems consistent with the disclosed embodiments may allow computer206 to request another system to execute this software and report itsresults to computer 206. Further, computer 206 may download neuralnetwork software from off-board systems prior to, or subsequent to, thenetwork being trained. In another embodiment, the payload determinationprocess may be performed for operations other than dump operations. Forinstance, computer 206 may perform payload determination processes whenmachine 10 is roading with a load, etc.

Further, an off-board system, as the term is used herein, may representa system that is located remote from machine 10. An off-board system maybe a system that connects to the machines through wireline or wirelessdata links. Further, an off-board system may be a computer systemincluding known computing components, such as one or more processors,software, display, and interface devices that operate collectively toperform one or more processes. Alternatively, or additionally, anoff-board system may include one or more communication devices thatfacilitate the transmission of data to and from the machines. In certainembodiments, an off-board system may be another machine remotely locatedfrom machine 10.

Additionally, although the disclosed embodiments are described as beingassociated with data and software programs stored in memory and otherstorage mediums, one skilled in the art will appreciate that theseembodiments may also be stored on or read from other types ofcomputer-readable media, such as secondary storage devices, like harddisks, floppy disks, optical storage devices, DVDs, or CD-ROM; a carrierwave from a communication link or network, such as the Internet; orother forms of RAM or ROM.

It is intended that the specification and examples be considered asexemplary only, with a true scope of the invention being indicated bythe following claims and their equivalents.

1. A monitoring system, comprising: a structural component configured toundergo mechanical loading; and a wireless node attached to thestructural component and including: a strain sensing device configuredto measure strain experienced by the structural component at thelocation of the node; and a processor configured to predict, based onthe strain measurements, fatigue life of the structural component. 2.The system of claim 1, wherein the system includes a network of at leasttwo wireless nodes, each node including: a strain sensing deviceconfigured to take strain measurements; and a processor configured toprocess information relating to the strain measurements.
 3. The systemof claim 2, wherein one or more of the wireless nodes are configured towirelessly transmit information relating to the strain measurements toat least one of the other wireless nodes.
 4. The system of claim 1,wherein the node is configured to wirelessly transmit, to a locationremote from the node, information relating to the strain measurements.5. The system of claim 1, wherein the node is configured to interfacewith a portable electronic device and transfer, to the portableelectronic device, information relating to the strain measurements.
 6. Amethod of evaluating fatigue in a structural component configured toundergo mechanical loading, comprising: taking measurements of strainexperienced by the structural component at a node attached to thestructural component; processing data collected regarding the strainmeasurements with a processor located at the node; and predicting, withthe processor, fatigue life of the structural component based on thestrain measurements.
 7. The method of claim 6, further including takingstrain measurements and processing strain measurement information at twoor more nodes.
 8. The method of claim 7, further including wirelesslytransmitting, from one or more of the wireless nodes, informationrelating to the strain measurements to at least one of the otherwireless nodes.
 9. The method of claim 6, further including wirelesslytransmitting, to a location remote from the node, information relatingto the strain measurements.
 10. The method of claim 6, further includinginterfacing the node with a portable electronic device and transferring,to the portable electronic device, information relating to the strainmeasurements.
 11. A machine, comprising: a power source; and amonitoring system, comprising: a structural component configured toundergo mechanical loading; and a node attached to the structuralcomponent and including: a strain sensing device configured to measurestrain experienced by the structural component at the location of thenode; and a processor configured to, based on the strain measurements,predict fatigue life of the structural component.
 12. The machine ofclaim 11, wherein the system includes a network of at least two wirelessnodes, each node including: a strain sensing device configured to takestrain measurements; and a processor configured to process informationrelating to the strain measurements; wherein one or more of the wirelessnodes are configured to wirelessly transmit information relating to thestrain measurements to at least one of the other wireless nodes.
 13. Themachine of claim 11, wherein the node is configured to wirelesslytransmit, to a location remote from the node, information relating tothe strain measurements.
 14. The machine of claim 11, wherein the nodeis configured to interface with a portable electronic device andtransfer, to the portable electronic device, information relating to thestrain measurements.
 15. The machine of claim 11, wherein the machineincludes more than one structural component which is monitored by themonitoring system; and wherein the monitoring system includes more thanone strain sensing device configured to monitor a structural component.